The collection and analysis of business intelligence data often includes the generation of reports and charts that portray sales, financial, production, and other metrics for a company at various points in time over a particular reporting period. These reports and charts may be prepared and analyzed on a monthly, quarterly, or annual basis. For example, a manufacturer and distributor of consumer products may prepare a report on a quarterly basis that charts the retail sales of their products in various sales regions as well as over the Internet. These sales figures may be presented in graphs that show the actual versus expected sales volume on a monthly basis for each product in a particular region.
If one of these graphs reveals an anomaly during the reporting period, the company will further analyze the related business intelligence data in an attempt to determine the cause of the anomaly. As an example, if the quarterly retail sales report for a company shows a sharp drop in sales in a region that was well below expectation for a particular product, an investigation into the cause may reveal a fire at the company's primary distribution center in the region that destroyed the existing inventory of the product, thus reducing the availability of the product to retailers. This fact, along with other contributing factors would normally be recorded in the quarterly report to explain the sharp drop in sales for the product, and the report would be archived in the company's records.
Investigation into such anomalies can be time consuming, however, because the events and factors that may have contributed to the anomaly are not usually recorded in a central location or in such a way that they may easily be searched based upon their relationship with the anomaly being investigated. For example, a news story or inventory damage report may exist regarding the distribution center fire in the scenario presented above, but it may not be stored in a location or categorized in such a manner that it could easily be found when searching for the cause of a sharp drop in retail sales.
In addition, even once the cause of the anomaly has been determined in the example above, the association of the distribution center fire and the resulting reduction in sales volume may only exist in the text of the archived quarterly report. Since the causal relationships between the fire, the resulting inventory loss, the lack of product availability, and the consequential drop in retail sales were not recorded in a manner that made them readily apparent and easy to find, these relationships may not be available to a subsequent investigation of potentially related events. For example, in a subsequent investigation of an overall drop in revenue for the company during the same period as the drop in retail sales, the causal relationships between the events leading up the drop in sales may have to be deduced again.
It is with respect to these considerations and others that the disclosure made herein is presented.